Query rewriting over lightweight ontologies, like DL-Lite ontologies, is a prominent approach for ontology-based data access. It is often the case in realistic scenarios that users ask an initial query which they later refine, e.g., by extending it with new constraints making their initial request more precise. So far, all DL-Lite systems would need to process the new query from scratch. In this paper we study the problem of computing the rewriting of an extended query by 'extending' a previously computed rewriting of the initial query and avoiding recomputation. Interestingly, our approach also implies a novel algorithm for computing the rewriting of a fixed query. More precisely, the query can be 'decomposed' into its atoms and then each atom can be processed incrementally. We present detailed algorithms, several optimisations for improving the performance of our query rewriting algorithm, and finally, an experimental evaluation.
Abstract-The Semantic Web can be viewed as largely about "Knowledge meets the Web". Thus its vision includes ontologies and rules. A key requirement for the architecture of the Semantic Web is to be able to layer "rules on top of ontologies" and "ontologies on top of rules". This has as a counterpart the definition of a mapping between Description Logics and Logic Programming, which is known as Description Logic Programs. In this paper we extend the Description Logic Programs with fuzzy sets and fuzzy logic in order to be able to represent the imprecision and vagueness of real-life applications. We provide the common semantics of the mapping, and the conditions that must be met for this semantic equivalence, based on the modeltheoretic semantics.
Fuzzy extensions to Description Logics (DLs) have gained considerable attention the last decade. So far most works on fuzzy DLs have focused on either very expressive languages, like fuzzy OWL and OWL 2, or on highly inexpressive ones, like fuzzy OWL 2 QL and fuzzy OWL 2 EL. To the best of our knowledge a fuzzy extension to the language OWL 2 RL has not been thoroughly studied so far. This language is very relevant since it combines both adequate expressive power as well as efficient reasoning algorithms which can be realised using rule-based (Datalog) technologies. In contrast to previous fuzzy extensions, a fuzzy extension of OWL 2 RL is not a straightforward task for the following reason. The main motivation of OWL 2 RL is that its axioms can be equivalently represented as Datalog rules. Hence, to achieve our goal we need to investigate which OWL 2 RL axioms when interpreted under the fuzzy setting can be transformed to equivalent fuzzy Datalog rules. We show that this is not, in general, possible for all axioms but we show that this "issue" can to a large extent be alleviated. Moreover, we have performed an experimental evaluation with many well-known ontologies which showed that such axioms are not used so often in practice.
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